Multiple change points detection and clustering in dynamic networks
نویسندگان
چکیده
منابع مشابه
ahp algorithm and un-supervised clustering in auto insurance fraud detection
this thesis is a study on insurance fraud in iran automobile insurance industry and explores the usage of expert linkage between un-supervised clustering and analytical hierarchy process(ahp), and renders the findings from applying these algorithms for automobile insurance claim fraud detection. the expert linkage determination objective function plan provides us with a way to determine whi...
15 صفحه اولDynamic detection of change points in long time series
We consider the problem of detecting change points (structural changes) in long sequences of data, whether in a sequential fashion or not, and without assuming prior knowledge of the number of these change points. We reformulate this problem as the Bayesian filtering and smoothing of a non standard state space model. Towards this goal, we build a hybrid algorithm that relies on particle filteri...
متن کاملDetection of Multiple Change–Points in Multivariate Time Series
We consider the multiple change–point problem for multivariate time series, including strongly dependent processes, with an unknown number of change–points. We assume that the covariance structure of the series changes abruptly at some unknown common change–point times. The proposed adaptive method is able to detect changes in multivariate i.i.d., weakly and strongly dependent series. This adap...
متن کاملAdaptive Detection of Multiple Change–Points in Asset Price Volatility
This chapter considers the multiple change–point problem for time series, including strongly dependent processes, with an unknown number of change– points. We propose an adaptive method for finding the segmentation, i.e., the sequence of change–points τ with the optimal level of resolution. This optimal segmentation τ̂ is obtained by minimizing a penalized contrast function J(τ , y)+βpen(τ ). Fo...
متن کاملUnifying Local and Global Change Detection in Dynamic Networks
Many real-world networks are complex dynamical systems, where both local (e.g., changing node attributes) and global (e.g., changing network topology) processes unfold over time. Local dynamics may provoke global changes in the network, and the ability to detect such effects could have profound implications for a number of real-world problems. Most existing techniques focus individually on eith...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics and Computing
سال: 2017
ISSN: 0960-3174,1573-1375
DOI: 10.1007/s11222-017-9775-1